基于横波分裂的裂缝属性识别研究

发布时间：2018-03-21 06:50

本文选题：横波分裂　切入点：裂缝属性识别　出处：《吉林大学》2015年硕士论文　论文类型：学位论文

【摘要】：随着我国经济的快速发展，，对油气资源的需求也逐年增大。由于优质构造型油气藏勘探、开发目标的不断减少，油气勘探的方向开始转向非常规油气藏。裂缝性油气藏分布范围广，已经成为一个重要的勘探领域。与其它类型的油气藏相比，裂缝性油气藏具有裂缝系统分布复杂、储层基质物性较差、渗透性较差、原油采收率较低的特点，因此裂缝性油气藏的开发具有相当大的难度，对裂缝属性的识别研究是当前油气勘探领域的重要课题之一。当横波在裂缝介质中传播时会发生横波分裂现象，分裂成偏振方向相互垂直的快横波和慢横波。在通过多层裂缝介质时，快横波和慢横波会发生再分裂现象，使波的传播过程更加复杂。利用横波分裂现象可以获得地层的裂缝走向和裂缝密度信息，对裂缝性油气藏研究有着重要意义。本文用使用二维三分量HTI介质正演模拟程序对横波在双层介质中的再分裂现象进行模拟，探讨了裂缝密度和裂缝方位角对横波再分裂现象的影响。在裂缝属性识别方法中Pearson相关系数法具有抗噪性强，稳定性高的特点。而Pearson相关系数法中对Pearson相关系数的求取是一个最优化问题，粒子群算法是一种解决非线性最优化问题的有效手段，因此本文将粒子群算法与Pearson相关系数法相结合对裂缝属性进行识别，通过迭代自动识别出裂缝方位角和时差。首先本文使用粒子群与Pearson相关系数相结合的方法对单道地震记录进行裂缝属性识别，结果表明该方法能对裂缝属性进行很好地识别。然后，为了验证该方法在噪声环境下的有效性，对加入噪音的多道地震记录其进行处理，并对识别结果进行统计分析，结果表明在噪声环境下该方法也能很好地对识别裂缝属性。通过将在噪声环境下多道地震数据的识别结果与互相关法进行对比，该方法的抗噪性更强，识别效果更好。最后对上层为各向同性介质下层为HTI介质的双层模型的地震记录进行属性识别也获得了良好的识别效果。
[Abstract]:With the rapid development of China's economy, the demand for oil and gas resources has increased year by year. The direction of oil and gas exploration begins to turn to unconventional oil and gas reservoirs. The fractured reservoirs are widely distributed and have become an important exploration field. Compared with other types of reservoirs, fractured reservoirs have complex distribution of fracture systems. Because of the poor physical properties of reservoir matrix, poor permeability and low oil recovery, the development of fractured reservoirs is very difficult, and the identification of fracture attributes is one of the most important topics in the field of oil and gas exploration. When the shear wave propagates in the fractured medium, the shear wave splits into fast shear wave and slow shear wave, which are perpendicular to each other in the polarization direction. The wave propagation process is more complicated, and the information of fracture strike and fracture density can be obtained by using the shear wave splitting phenomenon, which is of great significance to the study of fractured oil and gas reservoirs. In this paper, the phenomenon of S-wave resplitting in double-layer medium is simulated by using two-dimensional three-component HTI medium forward simulation program, and the influence of crack density and crack azimuth on S-wave resplitting is discussed. In the fracture attribute identification method, the Pearson correlation coefficient method has the characteristics of strong noise resistance and high stability. However, the calculation of Pearson correlation coefficient in the Pearson correlation coefficient method is an optimization problem. Particle swarm optimization (PSO) is an effective method to solve nonlinear optimization problem. Therefore, this paper combines PSO with Pearson correlation coefficient method to identify fracture attributes, and automatically identify the azimuth and time difference of cracks by iterative method. Firstly, the method of particle swarm optimization combined with Pearson correlation coefficient is used to identify the fracture attributes of single channel seismic records. The results show that the method can identify the fracture attributes well. In order to verify the effectiveness of the method in noisy environment, the multi-channel seismic records with noise are processed, and the recognition results are statistically analyzed. The results show that the method can also be used to identify fracture attributes in noisy environment. By comparing the recognition results of multi-channel seismic data in noisy environment with the cross-correlation method, the method is more robust to noise. Finally, a good recognition effect is obtained for the seismic records of a bilayer model with isotropic medium in the upper layer and a HTI medium in the lower layer.
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：P631.4;P618.13

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